mdf streamlit application

.streamlit/config.toml

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+[theme]
+primaryColor="#f6b17a"
+backgroundColor="#04043B"
+secondaryBackgroundColor="#424971"
+textColor="#ffffff"
+

app.py

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+import streamlit as st, pandas as pd, os, io
+from modeci_mdf.mdf import Model, Graph, Node, Parameter, OutputPort
+from modeci_mdf.utils import load_mdf_json, load_mdf, load_mdf_yaml
+from modeci_mdf.execution_engine import EvaluableGraph
+import json
+st.set_page_config(layout="wide")
+st.title("🔋 MDF Simulator")
+import requests
+# models: Purpose: To store the state of the model and update the model
+def run_simulation(param_inputs, mdf_model):
+    mod_graph = mdf_model.graphs[0]
+    nodes = mod_graph.nodes
+    for node in nodes:
+        parameters = node.parameters
+        outputs = node.output_ports
+        eg = EvaluableGraph(mod_graph, verbose=False)
+        duration = param_inputs["Simulation Duration (s)"]
+        dt = param_inputs["Time Step (s)"]
+        t = 0
+        times = []
+        output_values = {op.value: [] for op in outputs}
+        while t <= duration:
+            times.append(t)
+            if t == 0:
+                eg.evaluate()
+            else:
+                eg.evaluate(time_increment=dt)
+
+            for param in output_values:
+                eval_param = eg.enodes[node.id].evaluable_parameters[param]
+                output_values[param].append(eval_param.curr_value)
+            t += dt
+        chart_data = pd.DataFrame(output_values)
+        chart_data['Time'] = times
+        chart_data.set_index('Time', inplace=True)
+        return chart_data
+
+# views: Purpose: To display the state of the model 
+def show_simulation_results(chart_data):
+    st.line_chart(chart_data, use_container_width=True, height=400)
+    st.write("Output Values")
+    st.write(chart_data)
+
+def show_mdf_graph(mdf_model):
+    st.subheader("MDF Graph")
+    mdf_model.to_graph_image(engine="dot", output_format="png", view_on_render=False, level=3, filename_root=mdf_model.id, only_warn_on_fail=(os.name == "nt"))
+    image_path = mdf_model.id + ".png"
+    st.image(image_path, caption="Model Graph Visualization")
+
+def show_json_output(mdf_model):
+    st.subheader("JSON Output")
+    st.json(mdf_model.to_json())
+
+def view_tabs(mdf_model, param_inputs): # view
+    tab1, tab2, tab3 = st.tabs(["Simulation Results", "MDF Graph", "Json Output"])
+    with tab1:
+        mod_graph = mdf_model.graphs[0]
+        nodes = mod_graph.nodes
+        for node in nodes:
+            st.write("Node Name: ", node.id)
+            node_parameters = {}
+            for param in node.parameters:
+                node_parameters[param.id] = node_parameters[param.id] = param.value if param.value else param.time_derivative
+            st.write("Node Parameters: ", node_parameters)   
+            chart_data = run_simulation(param_inputs, mdf_model) # model
+            show_simulation_results(chart_data) # view
+    with tab2:
+        show_mdf_graph(mdf_model) # view
+    with tab3:
+        show_json_output(mdf_model) # view
+
+def parameter_form_to_update_model_and_view(mdf_model, parameters, param_inputs, mod_graph, nodes):
+    form = st.form(key="parameter_form")
+    valid_inputs = True
+
+    for i, param in enumerate(parameters):
+        if isinstance(param.value, str) or param.value is None:
+            continue  
+        key = f"{param.id}_{i}"
+        if mdf_model.metadata:
+            value = form.text_input(f"{param.metadata.get('description', param.id)} ({param.id})", value=str(param.value), key=key)
+        else:
+            value = form.text_input(f"{param.id}", value=str(param.value), key=key)
+        
+        try:
+            param_inputs[param.id] = float(value)
+        except ValueError:
+            st.error(f"Invalid input for {param.id}. Please enter a valid number.")
+            valid_inputs = False
+
+    sim_duration = form.text_input("Simulation Duration (s)", value=str(param_inputs["Simulation Duration (s)"]), key="sim_duration")
+    time_step = form.text_input("Time Step (s)", value=str(param_inputs["Time Step (s)"]), key="time_step")
+
+    try:
+        param_inputs["Simulation Duration (s)"] = float(sim_duration)
+    except ValueError:
+        st.error("Invalid input for Simulation Duration. Please enter a valid number.")
+        valid_inputs = False
+    try:
+        param_inputs["Time Step (s)"] = float(time_step)
+    except ValueError:
+        st.error("Invalid input for Time Step. Please enter a valid number.")
+        valid_inputs = False
+
+    run_button = form.form_submit_button("Run Simulation")
+    if run_button:
+        if valid_inputs:
+            for param in parameters:
+                if param.id in param_inputs:
+                    param.value = param_inputs[param.id]
+            view_tabs(mdf_model, param_inputs)
+        # else:
+        #     st.error("Please correct the invalid inputs before running the simulation.")
+
+
+def upload_file_and_load_to_model():
+    st.write("Choose how to load the model:")
+    load_option = st.radio("", ("Upload File", "GitHub URL"))
+
+    if load_option == "Upload File":
+        uploaded_file = st.file_uploader("Choose a JSON/YAML/BSON file", type=["json", "yaml", "bson"])
+        if uploaded_file is not None:
+            file_content = uploaded_file.getvalue()
+            file_extension = uploaded_file.name.split('.')[-1].lower()
+            return load_model_from_content(file_content, file_extension)
+    else:
+        st.write("sample_github_url = https://raw.githubusercontent.com/ModECI/MDF/development/examples/MDF/NewtonCoolingModel.json")
+        github_url = st.text_input("Enter GitHub raw file URL:", placeholder="Enter GitHub raw file URL")
+        if github_url:
+            try:
+                response = requests.get(github_url)
+                response.raise_for_status()
+                file_content = response.content
+            # print(file_content)
+                file_extension = github_url.split('.')[-1].lower()
+                return load_model_from_content(file_content, file_extension)
+            except requests.RequestException as e:
+                st.error(f"Error loading file from GitHub: {e}")
+                return None
+
+    return None
+
+def load_model_from_content(file_content, file_extension):
+    try:
+        if file_extension == 'json':
+            json_data = json.loads(file_content)
+            mdf_model = Model.from_dict(json_data)
+        elif file_extension in ['yaml', 'yml']:
+            mdf_model = load_mdf_yaml(io.BytesIO(file_content))
+        else:
+            st.error("Unsupported file format. Please use JSON or YAML files.")
+            return None
+        
+        st.session_state.original_mdf_model = mdf_model  # Save the original model
+        st.session_state.mdf_model_yaml = mdf_model  # Save the current model state
+        return mdf_model
+    except Exception as e:
+        st.error(f"Error loading model: {e}")
+        return None
+
+
+def main():
+    st.write("Text box changed to input. Github URL is allowed. Added some warnings eg. on adding text in input fields. Now working on multiple parameters allow.")
+    mdf_model = upload_file_and_load_to_model() # controller
+    if mdf_model:
+        mod_graph = mdf_model.graphs[0]
+        nodes = mod_graph.nodes
+        for node in nodes:
+            parameters = node.parameters
+            param_inputs = {}
+        if mdf_model.metadata:
+            preferred_duration = float(mdf_model.metadata.get("preferred_duration", 10))
+            preferred_dt = float(mdf_model.metadata.get("preferred_dt", 0.1))
+        else:
+            preferred_duration = 100
+            preferred_dt = 0.1
+        param_inputs["Simulation Duration (s)"] = preferred_duration
+        param_inputs["Time Step (s)"] = preferred_dt
+        parameter_form_to_update_model_and_view(mdf_model, parameters, param_inputs, mod_graph, nodes)
+
+if __name__ == "__main__":
+    main()
+
+
+

environment.yml

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+name: my_environment
+channels:
+  - defaults
+dependencies:
+  - python
+  - pip
+  - modeci_mdf
+  - streamlit
+  - matplotlib

packages.txt

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+graphviz

requirements.txt

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+streamlit 
+modeci_mdf
+matplotlib==3.5.1
+graphviz 
+ipython
+torch
+modelspec
+graph_scheduler
+streamlit-code-editor